www.nature.com/scientificreports OPEN Multimorbidity Analysis According to Sex and Age towards Cardiovascular Diseases of Adults in Received: 20 December 2017 Northeast China Accepted: 23 April 2018 Published: xx xx xxxx 1 1 1 2 2 1 1 Lina Jin , Xin Guo , Jing Dou , Binghui Liu , Jiangzhou Wang , Jiagen Li , Mengzi Sun , 1 1 1 Chong Sun , Yaqin Yu & Yan Yao Non-communicable diseases (NCDs) are great challenges in public health, where cardiovascular diseases (CVD) accounted for the large part of mortality that caused by NCDs. Multimorbidity is very common in NCDs especially in CVD, thus multimorbidity could make NCDs worse and bring heavy economic burden. This study aimed to explore the multimorbidity among adults, especially the important role of CVD that played in the entire multimorbidity networks. A total of 21435 participants aged 18–79 years old were recruited in Jilin province in 2012. Weighted networks were adopted to present the complex relationships of multimorbidity, and Charlson Comorbidity Index (CCI) was used to evaluate the burden of multimorbidity. The prevalence of CVD was 14.97%, where the prevalence in females was higher than that in males (P < 0.001), and the prevalences of CVD increased with age (from 2.22% to 38.38%). The prevalence of multimorbidity with CVD was 96.17%, and CVD could worsen the burden of multimorbidity. Multimorbidity and multimorbidity with CVD were more marked in females than those in males. And the prevalence of multimorbidity was the highest in the middle-age, while the prevalence of multimorbidity with CVD was the highest in the old population. Non-communicable diseases (NCDs) are believed as leading causes of death in the world, and regarded as major 1–3 threats to human health and sustainable development . Of these, 17.60 million people died from cardiovascular 4 5 diseases (CVD) in 2016 . In China, there were 230 million patients with CVD in 2010 , and the burden of CVD was also predicted to be a high level. Moreover, multimorbidity is very common in CVD, and it is reported that 6,7 more than 50% CVD patients suffer from at least one additional disease . Multimorbidity not only ae ff cts the quality of life among CVD patients, but also can bring heavy economic burden to individuals, families and the society . Many occurrences of multimorbidity with CVD had been recognized and investigated in previous studies, 9–11 and hypertension was one of the most widely studied occurrences of multimorbidity with CVD . World Health Organization (WHO) pointed out that approximately 13% of CVD deaths were accounted for hypertension . 13,14 15 16,17 Diabetes , obesity , and dyslipidemia were also recognized as the common occurrences of multimorbidity 18 19 20 with CVD. Besides, some other diseases such as chronic respiratory disease , liver disease and depression had been studied as potential occurrences of multimorbidity with CVD as well. Moreover, some studies also showed that the pattern of multimorbidity of CVD was different among groups 6,21–23 stratified by sex and age . A study documented that the prevalence of anemia was the highest in female patients with heart failure, whereas the prevalence of dyslipidemia was the highest in male patients with heart failure . Another study found that there were gender differences in the pattern of multimorbidity with CVD, and male patients were more likely to have multimorbidity . However, previous studies concerned only on one single occurrence of multimorbidity, rather than the over- all occurrences of multimorbidity that covered information of multiple NCDs. In this study, we explored and presented the multimorbidity among adults in Jilin province, northeast China (Latitude 40°~46°, Longitude 121°~131°) in 2012, especially the important role of CVD that played in the entire multimorbidity networks. 1 2 School of Public Health, Jilin University, Changchun, Jilin, 130021, China. Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, 130024, China. Correspondence and requests for materials should be addressed to Y. Ya (email: firstname.lastname@example.org) Scientific REPO R TS | (2018) 8:8607 | DOI:10.1038/s41598-018-25561-y 1 www.nature.com/scientificreports/ Gender Age Male Female Young Middle-age Old 2 2 Rank Disease Total (n = 10337) (n = 11098) χ P-value (n = 6657) (n = 12980) (n = 1798) χ P-value 1 Hyperlipidemia 10951(51.09) 5379(52.04) 5572(50.21) 7.166 0.007 2288(34.37) 7504(57.81) 1159(64.46) 1108.124 <0.001 2 Hypertension 7511(35.04) 3893(37.66) 3618(32.60) 60.209 <0.001 892(13.40) 5470(42.14) 1149(63.90) 2315.335 <0.001 3 CVD 3209(14.97) 1213(11.73) 1996(17.99) 164.269 <0.001 148(2.22) 2371(18.27) 690(38.38) 1734.311 <0.001 4 Obesity 3105(14.49) 1529(14.79) 1576(14.20) 1.508 0.219 846(12.71) 2034(15.67) 225(12.51) 37.322 <0.001 5 Disc disease 2893(13.50) 1117(10.81) 1776(16.00) 123.814 <0.001 456(6.85) 2175(16.76) 262(14.57) 371.832 <0.001 6 Diabetes 1956(9.13) 972(9.40) 984(8.87) 1.859 0.173 143(2.15) 1488(11.46) 325(18.08) 650.085 <0.001 7 Rheumatoid arthritis 1942(9.06) 638(6.17) 1304(11.75) 202.102 <0.001 192(2.88) 1453(11.19) 297(16.52) 501.314 <0.001 8 Gastroenteritis 1923(8.97) 845(8.17) 1078(9.71) 15.521 <0.001 570(8.56) 1218(9.38) 135(7.51) 8.778 0.012 9 Cholecystitis 1585(7.39) 354(3.42) 1231(11.09) 459.494 <0.001 229(3.44) 1217(9.38) 139(7.73) 226.747 <0.001 10 Gynecological inflammation 1252(5.84) 0 1252(11.28) — — 460(14.92) 770(10.89) 22(2.38) 117.454 <0.001 11 Gastric ulcer or duodenal ulcer 819(3.82) 427(4.13) 392(3.53) 5.219 0.022 164(2.46) 570(4.39) 85(4.73) 48.890 <0.001 12 Chronic bronchitis 767(3.58) 331(3.20) 436(3.93) 8.188 0.004 82(1.23) 544(4.19) 141(7.84) 215.102 <0.001 Prostate hyperplasia or 13 648(3.02) 648(6.27) 0 — — 43(1.20) 464(7.85) 141(16.53) 334.018 <0.001 inflammation 14 Anemia 594(2.77) 72(0.70) 522(4.70) 318.934 <0.001 247(3.71) 309(2.38) 38(2.11) 32.030 <0.001 15 Fatty liver 510(2.38) 267(2.58) 243(2.19) 3.566 0.059 100(1.50) 377(2.90) 33(1.84) 39.753 <0.001 16 Calculus in urinary system 481(2.24) 271(2.62) 210(1.89) 12.981 <0.001 90(1.35) 348(2.68) 43(2.39) 35.629 <0.001 17 Rheumatic 442(2.06) 108(1.04) 334(3.01) 102.302 <0.001 63(0.95) 320(2.47) 59(3.28) 64.721 <0.001 18 Nasopharyngitis 439(2.05) 233(2.25) 206(1.86) 4.223 0.040 155(2.33) 257(1.98) 27(1.50) 5.583 0.061 19 Cataract 402(1.88) 113(1.09) 289(2.60) 66.392 <0.001 4(0.06) 219(1.69) 179(9.96) 759.591 <0.001 20 Gallstone 384(1.79) 125(1.21) 259(2.33) 38.466 <0.001 43(0.65) 284(2.19) 57(3.17) 80.677 <0.001 Table 1. Sex-specific and age-specific distributions of top 20 NCDs of participants (n = 21435) in Northeast China [n (%)]. e p Th eople with age less than 40 (age ≤ 40) were viewed as young, and people with 41 ≤ age ≤ 65 b 2 and age ≥ 66 were middle-age and old, respectively. The χ and the p-value were calculated among women c 2 only; the χ and the p value were calculated among men only. Weighted networks were adopted to demonstrate the complex relationships of co-occurrence of NCDs. The prev- alences of multimorbidity and multimorbidity with CVD were more marked in females than those in males, and the prevalences were extremely high in the old population. We found that multimorbidity was extremely common among CVD patients, and CVD would also worsen the burden of multimorbidity. Results Sex-specific and age-specific distributions of top 20 NCDs of participants. Table 1 presents the top 20 NCDs with the highest prevalences, and the ranks of these diseases were a little different by sex and age. The prevalences of majority diseases were different by sex and age as well (P < 0.05). The prevalence of CVD was 14.49% (ranked the third), where in females it was higher than that in males, and the prevalences of CVD increased with age (from 2.22% to 38.38%). Sex-specific and age-specific top 5 patterns of multimorbidity. Table 2 shows the sex-specific and age-specific prevalences of top 5 patterns of multimorbidity, with the pair hyperlipidemia & hypertension as the highest one. Generally, the prevalences of CVD & hyperlipidemia and CVD & hypertension were also very high, especially in the old population. Sex-specific and age-specific top 5 patterns of multimorbidity with CVD. Multimorbidity was extremely common among CVD patients, where there were 96.17% (3086/3209) CVD patients suffered from at least one other NCDs. Further, the prevalence of multimorbidity with CVD in females (97.29%) was more marked than that in males (94.31%), and it was the worst among old (97.54%) CVD patients (P < 0.001, 85.81% for the young and 96.42% for the middle-age). The top 5 patterns of sex-specific and age-specific multimorbidity with CVD were shown in Table 3. In general, the ranks for multimorbidity with CVD were similar, where hyper- lipidemia and hypertension were the most frequent occurrences of multimorbidity among CVD patients, thus the “CVD-Hyperlipidemia-Hypertension” (CVD-H-H) triangle was inclined to play an important role in the multimorbidity networks. Evaluation of multimorbidity and multimorbidity networks. Figures 1–3 showed the networks of the multimorbidity in the whole population, as well as the sex-specific and age-specific populations, and Table 4 list the indices which could measure the features of the networks. The network density and the average degree of females were larger than those of males, thus the network of females was much denser than that of males, i.e., the NCDs in females tended to co-occur more frequently than those in males. Meanwhile, the network density (as well as the average degree) reached the largest in the middle-age, and smallest in the young. In Table 4, each average degree of the CVD-H-H triangle was extremely higher than that of its network, where the average degree Scientific REPO R TS | (2018) 8:8607 | DOI:10.1038/s41598-018-25561-y 2 www.nature.com/scientificreports/ Group Top 1 Top 2 Top 3 Top 4 Top 5 Hyperlipidemia & Obesity & CVD & CVD & Obesity & Hypertension Hyperlipidemia Hyperlipidemia Hypertension Hypertension Total — 5015(23.40) 2183(10.18) 2065(9.63) 1913(8.92) 1759(8.21) Hyperlipidemia & Obesity & Obesity & CVD & CVD & Hypertension Hyperlipidemia Hypertension Hypertension Hyperlipidemia Male 2564(24.80) 1101(10.65) 870(8.42) 782(7.57) 751(7.27) Sex Hyperlipidemia & CVD & Obesity & Hyperlipidemia CVD & Hypertension Hypertension Hyperlipidemia Hyperlipidemia & Disc disease Female 2451(22.09) 1314(11.84) 1131(10.19) 1082(9.75) 1051(9.47) Hyperlipidemia Hyperlipidemia & Obesity & Obesity & Hyperlipidemia Hypertension Hyperlipidemia Hypertension & Disc disease Young Gastroenteritis 528(7.93) 519(7.80) 282(4.24) 211(3.17) 196(2.94) Hyperlipidemia & CVD & Obesity & CVD & Hyperlipidemia Age Hypertension Hyperlipidemia Hyperlipidemia Hypertension & Disc disease Middle-age 3708(28.57) 1543(11.89) 1488(11.46) 1380(10.63) 1321(10.18) Hyperlipidemia & CVD & Diabetes & Diabetes & CVD & Hypertension Hypertension Hyperlipidemia Hypertension Hyperlipidemia Old 779(45.58) 490(28.67) 472(27.62) 236(13.81) 232(13.58) Table 2. Sex-specific and age-specific top 5 patterns of multimorbidity [n (%)]. e p Th eople with age less than 40 (age ≤ 40) were viewed as young, and people with 41 ≤ age ≤ 65 and age ≥ 66 were middle-age and old, respectively. Group Top 1 Top 2 Top 3 Top 4 Top 5 Hyperlipidemia Hypertension Disc disease Rheumatoid arthritis Obesity Total — 2065(9.63) 1913(8.92) 806(3.76) 639(2.98) 611(2.85) Hypertension Hyperlipidemia Disc disease Diabetes Obesity Male 782(7.57) 751(7.27) 235(2.27) 215(2.08) 214(2.07) Sex Hyperlipidemia Hypertension Disc disease Rheumatoid arthritis Obesity Female 1314(11.84) 1131(10.19) 571(5.15) 479(4.32) 397(3.58) Hyperlipidemia Hypertension Disc disease Gastroenteritis Gynecological inflammation Young 50(0.75) 43(0.62) 41(0.65) 29(0.44) 29(0.44) Hyperlipidemia Hypertension Disc disease Obesity Rheumatoid arthritis Age Middle-age 1543(11.89) 1380(10.63) 627(4.83) 479(3.69) 468(3.61) Hypertension Hyperlipidemia Rheumatoid arthritis Disc disease Diabetes Old 490(27.25) 472(26.25) 157(8.73) 138(7.68) 133(7.40) Table 3. Sex-specific and age-specific top 5 patterns of multimorbidity among CVD patients [n (%)]. The people with age less than 40 (age ≤ 40) were viewed as young, and people with 41 ≤ age ≤ 65 and age ≥ 66 were middle-age and old, respectively. of the triangle in old population was 6.9 (22.67/3.27) times of its own network (Fig. 3(c)), which was the largest. Meanwhile, the proportion of CVD that contributed to the CVD-H-H triangle in old population was also the largest (11/(11 + 12 + 11) = 32.35%). The average degree of the CVD-H-H triangle in females was more marked than that in males, but compared with the corresponding female/male network, the CVD-H-H triangle in males was more important, which was 6.6 (24.67/3.74) times of its network (Fig. 2(b)). However, the proportion of CVD that contributed to the CVD-H-H triangle in males (8/(8 + 14 + 15) = 21.62%) was smaller than that in females (14/(14 + 21 + 13) = 29.17%). Finally, the severity of the multimorbidity using Charlson Comorbidity Index (CCI) was also shown in Table 4. It was no surprising that the CCI in the population with CVD was extremely larger than that without CVD in all groups (P < 0.001), which indicated that CVD would bring extra burden to multimorbidity. Further, the CCIs in males were smaller than those in females (P = 0.013 for CVD and P = 0.002 for non-CVD), and CCIs were the highest among the elderly, and the lowest among the young (all P < 0.001). Discussion NCDs are believed to bring great challenges to and have important impacts on public health nowadays, which accounted for 63% of deaths worldwide in 2008, and CVD is one of the most important main causes of deaths . 7,25 Meanwhile, multimorbidity was extremely common among the CVD patients . In this study, we investigated the multimorbidity of 57 kinds of NCDs based on 21435 adults in Jilin province in 2012, especially the multimor- bidity with CVD. Hyperlipidemia, hypertension and CVD were top 3 NCDs with the highest prevalences in Jilin province. Multimorbidity and the multimorbidity with CVD were more marked in females than those in males. e p Th revalence of multimorbidity was the highest in middle-age, whereas the prevalence of multimorbidity with Scientific REPO R TS | (2018) 8:8607 | DOI:10.1038/s41598-018-25561-y 3 www.nature.com/scientificreports/ Figure 1. Multimorbidity network for the whole population, where “Prostate” represented “Prostate hyperplasia or inflammation”, “Gynecological” represented gynecological inflammation, “Gastric ulcer” represented gastric ulcer or duodenal ulcer; “other tumor” represented tumors except 9 cancer like liver cancer, lung cancer, etc. CVD was the highest in the old population. 96.17% CVD patients suffered from multimorbidity, where the prev- alence of multimorbidity increased with age, and CVD would worsen the burden of multimorbidity. Hyperlipidemia, hypertension and CVD were top 3 NCDs with the highest prevalences in Jilin province, 5,26 regardless of sex and age. e Th prevalence of CVD was 14.97%, which was lower than other studies in literature , due to that hypertension was not included in CVD in this study. Although CVD ranked the third, the analysis of CCI suggested that CVD would bring extra burden to multimorbidity and increase the 10-year mortality , thus the lethality and the burden of CVD with multimorbidity was much higher. Besides, among the top 5 patterns of multimorbidity there were 2 patterns of multimorbidity with CVD, which suggested that multimorbidity with CVD were very common. And 96.17% CVD patients suffered from multimorbidity, which was higher than other 22,23 studies , one possible reason might be that hyperlipidemia was investigated in our study. Further, the CVD-H-H triangle in males was more marked than that in females, relative to their own net- work, but CVD in males contributed less proportions to the triangle than that in females. It was suggested that hyperlipidemia and hypertension in males played more important roles in multimorbidity, while CVD and its 28,29 multimorbidity were more common in females, and would bring more risk to females . Therefore, different strategies should be developed to prevent NCDs and their multimorbidity in males and females separately. Finally, the prevalences of majority multimorbidity were the highest in the eldly, and the lowest in the young, 30,31 which were consistent with other studies . The possible reason might be that body immunity and function declined with age, so that the old people were more vulnerable to NCDs and their multimorbidity . Although the middle-age had a denser multimorbidity network, the CVD-H-H triangle in the old population played a more important role, relative to their own network, where there CVD occupied large percentage compared with that of the young and middle-age. u Th s it suggested different key prevention towards different age groups: multimorbid- ity with CVD were tended to cluster in the old population, while nutritional or metabolic diseases were common for young people . Some limitations should be noted here. Firstly, the participants in the study were selected in Jilin province, which could not represent the (CVD) multimorbidity in other places. Secondly, the disease situations were mainly based on self-report, which might cause bias. Thirdly, only cerebrovascular disorders, angina pectoris, coronary disease and myocardial infarction were involved in CVD, which might underestimate the prevalence of CVD and its multimorbidity. Finally, only sex and age were investigated in the study, but other factors that might have effects on the multimorbidity were worthy of further study. Methods Study population. Data were derived from a cross-sectional survey in Jilin Province of China in 2012, and the multistage stratified cluster sampling method was used to select the study samples. A total of 23050 partici- pants who had lived in Jilin Province for more than 6 months and were 18–79 years old were selected (see more details in Part 1 of the Supplementary Material). For the purpose of the present analyses, some subjects were excluded due to missing values (1615 subjects). Finally, a total of 21435 subjects were included in the present analyses. Ethics Statement. e Th ethics committee of the School of Public Health, Jilin University (Reference Number: 2012-R-011) and the Bureau of Public Health of Jilin Province (Reference Number: 2012–10) approved the study. All research methods followed the guidelines of investigation and written informed consent was obtained from all of the participants before data collection. Scientific REPO R TS | (2018) 8:8607 | DOI:10.1038/s41598-018-25561-y 4 www.nature.com/scientificreports/ Figure 2. Multimorbidity networks by sex, where (a) for males and (b) for females, “Prostate” represented “Prostate hyperplasia or inflammation”, “Gynecological” represented gynecological inflammation, “Gastric ulcer” represented gastric ulcer or duodenal ulcer; “other tumor” represented tumors except 9 cancer like liver cancer, lung cancer, etc., and “other digestive” represented other diseases of digestive system except 7 ones like fatty liver, cirrhosis, etc. CCI Network Average Average degree of Group density degree CVD-H-H CVD non-CVD Z P-value Total — 0.305 5.80 29.33 (10, 19, 15) 3.80 ± 1.62 1.19 ± 1.34 72.26 <0.001 Male 0.170 3.74 24.67 (8, 14, 15) 3.71 ± 1.54 1.20 ± 1.31 45.41 <0.001 Sex Female 0.296 6.52 32.00 (14, 21, 13) 3.86 ± 1.66 1.18 ± 1.38 55.82 <0.001 Young 0.065 1.36 8.00 (0, 8, 4) 1.72 ± 1.03 0.29 ± 0.56 21.99 <0.001 Age Middle-age 0.303 6.36 32.00 (11, 21, 16) 3.52 ± 1.44 1.48 ± 1.24 55.15 <0.001 Old 0.156 3.27 22.67 (11, 12, 11) 5.22 ± 1.26 3.74 ± 1.08 23.09 <0.001 Table 4. Evaluation of multimorbidity and multimorbidity networks. e p Th eople with age less than 40 (age ≤ 40) were viewed as young, and people with 41 ≤ age ≤ 65 and age ≥ 66were middle-age and old, respectively. CVD-H-H refers to the “CVD-Hyperlipidemia-Hypertension” triangle in the network, and the values in the brackets are degrees of CVD, hyperlipidemia and hypertension. Data collection and measurement. The data of this study included demographics, anthropometric measurements (e.g., height, weight, blood pressure) and NCDs situations (57 NCDs, including liver cancer, lung cancer, gastric cancer, colorectal cancer, breast cancer, cervical cancer, prostate cancer, thyroid carcinoma, leu- kemia and other tumor (except the above 9 ones); anemia, rheumatic and other hematologic and immune related Scientific REPO R TS | (2018) 8:8607 | DOI:10.1038/s41598-018-25561-y 5 www.nature.com/scientificreports/ Figure 3. Multimorbidity networks by age group, where (a) for young (age ≤ 40), (b) for middle-age (41 ≤ age ≤ 65), and (c) for old (age ≥ 66), “Prostate” represented “Prostate hyperplasia or inflammation”, “Gynecological” represented gynecological inflammation, “Gastric ulcer” represented gastric ulcer or duodenal ulcer; “other tumor” represented tumors except 9 cancer like liver cancer, lung cancer, etc., and “other digestive” represented other diseases of digestive system except 7 ones like fatty liver, cirrhosis, etc. diseases (except the above 2); obesity, diabetes, hyperlipidemia, thyrotoxicosis, osteoporosis, gout and other endocrine, nutritional and metabolic diseases (except the above 6 ones); schizophrenia, depression and other mental & behavioral disorders (except the above 2); cognition disorders, epilepsy, Parkinson’s disease and other neurological diseases (except the above 3 ones); cataract, glaucoma and other eye diseases (except the above 2); Scientific REPO R TS | (2018) 8:8607 | DOI:10.1038/s41598-018-25561-y 6 www.nature.com/scientificreports/ hypertension, CVD (including cerebrovascular disorders, angina pectoris, coronary heart disease and myocardial infarction), corpulmonale, varicose veins of lower extremity and other diseases of circulatory system (except the above 4 ones); chronic obstructive pulmonary emphysema, asthma, nasopharyngitis, chronic bronchitis, and other respiratory diseases (except the above 4 ones); gastric ulcer or duodenal ulcer, fatty liver, cirrhosis, chole- cystitis, gallstone, gastroenteritis, hernia of abdominal cavity, and other diseases of digestive system (except the above 7 ones); rheumatoid arthritis, disc disease, and other musculoskeletal and connective tissue diseases(except the above 2); nephritis, gynecological inflammation, breast diseases, urinary calculus, prostate hyperplasia or inflammation and other diseases of genitourinary system (except the above 5 ones)). After 12 or more hours of overnight fasting, finger-tip blood samples were taken from the subjects, and the plasma glucose (FPG) level was analyzed; the 2-hour FPG level was also tested. These tests were conducted by a Bayer Bai Ankang fingertip blood glucose monitor machine. The serum lipids, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL-C) and low-density lipoprotein (LDL-C), were measured before breakfast, using enzymatic methods in a central laboratory with standardized testing. Weight and height were performed after removing shoes and heavy outer clothing. Weights were measured to the nearest 0.1 kg using a calibrated scale with the subjects standing in an upright position, and heights were measured to the near- 2 2 est 0.1 cm using a standard anthropometer. Body mass index (BMI) was calculated as weight/height (kg/m ). Blood pressure was measured using mercury sphygmomanometer in the sitting position aer a 5-min r ft est period by trained professionals. Two readings each of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded, and the average of each measurement was used for data analysis. If the first two measurements 34–36 differed by more than 5 mmHg, additional readings were taken . Assessment criteria of disease. Hypertension was referred to those with SBP ≥ 140 mm Hg, and/or DBP ≥ 90 mm Hg, and/or normotensives treated with antihypertensive medications, and/or a self-reported his- tory of hypertension . Hyperlipidemia was defined as TC ≥ 5.18 mmol/L, and/or LDL-C ≥ 3.37 mmol/L, and/or HDL-C < 1.04 mmol/L, and/or TG ≥ 1.70 mmol/L, and/or normolipidemic subjects treated with antihyperlipi- demia medications, and/or with history of hyperlipidemia diseases . Obesity was defined as the BMI ≥ 28 kg/ 2 39 m . Diabetes was defined as FPG ≥ 7.0 mmol/l (126 mg/dl) and/or a self-reported history of diabetes. CVD was defined as a participant carried at least one of the following disease: cerebrovascular disorders, angina pectoris, coronary heart disease and myocardial infarction. Other NCDs were judged only by self-reported history of dis- eases which diagnosed in hospitals on county level and above. Statistical analysis. The continuous variables were expressed as means ± standard deviations (SD) and compared using the t test or Wilcoxon rank-sum test. The categorical variables were expressed as counts or per - centages and compared using the Rao-Scott-χ test. All statistical analyses were performed with R version 3.4.1 (University of Auckland, Oakland, New Zealand). Statistical significance was set at a P value < 0.05. In this study, weighted networks were applied to study the relationships among multimorbidity. The nodes of the network represented the diseases, and the height of each node was proportional to the prevalence of each disease. The edge in the network represented the co-occurrence of a multimorbidity pair, and the weight of the edge was proportional to the prevalence of each multimorbidity pair. When a participant carried more than 2 dis- eases, the count of every multimorbidity pair would have an increment of 1 (e.g., when a participant carried CVD, hypertension and hyperlipidemia, then the multimorbidity pair CVD & hypertension, hypertension & hyperlipi- demia and hyperlipidemia & CVD would have an increment of 1). The prevalence of disease or multimorbidity pair were calculated as the total counts of participants which carried the disease or multimorbidity pair divided by the corresponding sample size. The relationships of the multimorbidity with prevalence higher than 1% were list in the networks in our study. Degree was adopted to measure the centrality of a disease (e.g. CVD), where degree was the number of nodes that a focal node was connected to, which measured the involvement of the node in the network. Network density and average degree were used to evaluate the sparsity of a network. The network density of an undirected graph with N nodes and M edges was defined as 2 M/N(N − 1), which described the portion of the potential connec- tions (N(N − 1)/2) in a network that were actual connections (M). The average degree was defined as the average 40,41 of degrees of all nodes. The larger the network density (or average degree), the denser the network . CCI was used to measure the burden of multimorbidity or comorbidity, which had been validated in many clinical settings 42,43 to describe the condition of comorbidity and multimorbidity . The larger the CCI, the worse the condition of multimorbidity (the larger 10-year mortality). References 1. Clark, H. 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Author Contributions Conceived and designed the experiments: Lina Jin, Yan Yao, Yaqin Yu. Data curation and analysis: Lina Jin, Xin Guo, Yan Yao. Performed the weighted networks: Lina Jin, Binghui Liu, Jiangzhou Wang, Jiagen Li. Wrote the original draft of the manuscript: Xin Guo, Jing Dou, Mengzi Sun, Chong Sun. Contributed to reviewing and editing of the manuscript: Lina Jin, Yan Yao, Xin Guo, Jing Dou. Agree with the manuscript’s results and conclusions: Lina Jin, Xin Guo, Jing Dou, Jiagen Li, Mengzi Sun, Chong Sun, Yan Yao. All authors have read, and confirm that they meet, ICMJE criteria for authorship. Scientific REPO R TS | (2018) 8:8607 | DOI:10.1038/s41598-018-25561-y 8 www.nature.com/scientificreports/ Additional Information Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-25561-y. Competing Interests: The authors declare no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2018 Scientific REPO R TS | (2018) 8:8607 | DOI:10.1038/s41598-018-25561-y 9
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